Genetic analysis refers to the determination of the nucleotide sequence of a gene or genes of interest in a subject organism, including methods for analysis of one site of sequence variation (i.e., genotyping methods) and methods for analysis of a collection of sequence variations (haplotyping methods). Genetic analysis further includes methods for correlating sequence variation with disease risk, diagnosis, prognosis or therapeutic management.
At present, DNA diagnostic testing is largely concerned with identification of rare polymorphisms related to Mendelian traits. These tests have been in use for well over a decade. In the future genetic testing will come into much wider clinical and research use, as a means of making predictive, diagnostic, prognostic and pharmacogenetic assessments. These new genetic tests will in many cases involve multigenic conditions, where the correlation of genotype and phenotype is significantly more complex than for Mendelian phenotypes. To produce genetic tests with the requisite accuracy will require new methods that can simultaneously track multiple DNA sequence variations at low cost and high speed, without compromising accuracy. The ideal tests will be relatively inexpensive to set up and run, while providing extremely high accuracy, and, most important, enabling sophisticated genetic analysis.
Genotypes
The association of specific genotypes with disease risk, prognosis, and diagnosis as well as selection of optimal therapy for disease are some of the benefits expected to flow from the human genome project. At present, the most common type of genetic study design for testing the association of genotypes with medically important phenotypes is a case control study where the frequencies of variant forms of a gene are measured in one or more phenotypically defined groups of cases and compared to the frequencies in controls. (Alternatively, phenotype frequencies in two or more genotypically defined groups are compared.) The majority of such published genetic association studies have focused on measuring the contribution of a single polymorphic site (usually a single nucleotide polymorphism, abbreviated SNP) to variation in a medically important phenotype or phenotypes. In these studies one polymorphism serves as a proxy for all variation in a gene (or even a cluster of adjacent genes).
Recent articles (e.g., Terwilliger and Weiss. Linkage disequilibrium mapping of complex disease: fantasy or reality? Current Opinion in Biotechnology 9: 578-594, 1998) have drawn attention to the low degree of reproducibility of most association studies using single polymorphic sites. Some of the reasons for the lack of reproducibility of many association studies are apparent. In particular, the extent of human DNA polymorphism—most genes contain 10 or more polymorphic sites, and many genes contain over 100 polymorphic sites—is such that a single polymorphic site can only rarely serve as a reliable proxy for all variation in a gene (which typically covers at least several thousand nucleotides and can extend over 1,000,000 nucleotides). Even in cases where one polymorphic site is responsible for significant biological variation, there is no reliable method for identifying such a site. Several recent studies have begun to outline the extent of human molecular genetic variation. For example, a comprehensive survey of genetic variation in the human lipoprotein lipase (LPL) gene (Nickerson, D. A., et al. Nature Genetics 19: 233-240, 1998; Clark, A. G., et al. American Journal of Human Genetics 63: 595-612, 1998) compared 71 human subjects and found 88 varying sites in a 9.7 kb region. On average any two versions of the gene differed at 17 sites. This and other studies show that sequence variation may be present at approximately 1 in 100 nucleotides when 50 to 100 unrelated subjects are compared. The implications of the this data are that, in order to create genetic diagnostic tests of sufficient specificity and selectivity to justify widespread medical use, more sophisticated methods are needed for measuring human genetic variation.
Beyond tests that measure the status of a single polymorphic site, the next level of sophistication in genetic testing is to genotype two or more polymorphic sites and keep track of the genotypes at each of the polymorphic sites when calculating the association between genotypes and phenotypes (e.g., using multiple regression methods). However, this approach, while an improvement on the single polymorphism method in terms of considering possible interactions between polymorphisms, is limited in power as the number of polymorphic sites increases. The reason is that the number of genetic subgroups that must be compared increases exponentially as the number of polymorphic sites increases. In a medical study of fixed size this has the effect of dramatically increasing the number of groups that must be compared, while reducing the size of each subgroup to a small number. The consequence of these effects is an unacceptable loss of statistical power. Consider, for example, a clinical study of a gene that contains 10 variable sites. If each site is biallelic then there are 210 or 1024 possible combinations of polymorphic sites. If the study population is 500 subjects then it is likely that many genetically defined subgroups will contain only a small number of subjects. Thus, consideration of multiple polymorphisms (as can be determined from DNA sequence data, for example) does not get at the problem that the DNA sequence from a diploid subject does not sufficiently constrain the sequence of the subject's two chromosomes to be very useful for statistical analysis. Only direct determination of the DNA sequence on each chromosome (a haplotype) can constrain the number of genetic variables in each subject to two (allele 1 and allele 2), while accounting for all, or preferably at least a substantial subset of, the polymorphisms.
Haplotypes
A much more powerful measure of variation in a DNA segment than a genotype is a haplotype—that is, the set of polymorphisms that are found on a single chromosome.
In mammals, as in many other organisms, there are two copies (alleles) of each gene in every cell (except some genes which map to the sex chromosomes —X and Y in man). One allele is inherited from each parent. In general the two alleles in any organism are substantially similar in sequence, with polymorphic sites occurring less than every 100 nucleotides, and in some cases in less than every 1,000 nucleotides. Determination of the sequence of the non-variant nucleotide positions is not relevant to haplotyping. Thus, haplotyping comes down to determining the identity (e.g., the nucleotide sequence) of the polymorphisms on each of the two alleles at the polymorphic sites. For a subject that is heterozygous at two sites, where polymorphic site #1 is A or C, and polymorphic site #2 is G or T, we wish to know if the alleles are A-G and C-T, or if they are A-T and C-G. When DNA is extracted from a diploid organism the two alleles are mixed together in the same test tube at a 1:1 ratio. Thus, DNA analysis procedures performed on total genomic DNA, such as DNA sequencing or standard genotyping procedures which query the status of polymorphic sites one at a time, do not provide information required to determine haplotypes from DNA samples that are heterozygous at two or more sites.
Because of the evolutionary history of human populations, only a small fraction of all possible haplotypes (given a set of polymorphic sites at a locus) actually occur at appreciable frequency. For example, in a gene with 10 polymorphic sites only a small fraction—perhaps in the range of 1%—of the 1,024 possible genotypes is likely to exist at a frequency greater than 5% in a human population. Further, as described below, haplotypes can be clustered in groups of related sequences to facilitate genetic analysis. Thus determination of haplotypes is a simplifying step in performing a genetic association study (compared to the analysis of multiple polymorphisms), particularly when applied to DNA segments characterized by many polymorphic sites. There is also a potent biological rationale for sorting genes by haplotype, rather than by genotype at one polymorphic site: polymorphic sites on the same chromosome may interact in a specific way to determine gene function. For example, consider two sites of polymorphism in a gene, both of which encode amino acid changes. The two polymorphic residues may lie in close proximity in three dimensional space (i.e., in the folded structure of the encoded protein). If one of the polymorphic amino acids encoded at each of the two sites has a bulky side chain and the other has a small side chain then one can imagine a situation in which proteins that have either [bulky-small], [small-bulky] or [small-small] pairs of polymorphic residues are fully functional, but proteins with [bulky-bulky] residues at the two sites are impaired, due to a disruptive shape change caused by the interaction of the two bulky side groups. Now consider a subject whose genotype is heterozygous bulky/small at both polymorphic sites. The possible haplotype pairs in such a subject are [bulky-small]/[small-bulky], or [small-small]/[bulky-bulky]. The functional implications of these two haplotype pairs are quite different: active/active or active/inactive, respectively. A genotype test would simply reveal that the subject is doubly heterozygous. Only a haplotype test would reveal the biologically consequential structure of the variation. The interaction of polymorphic sites need not involve amino acid changes, of course, but could also involve virtually any combination of polymorphic sites.
The genetic analysis of complex traits can be made still more powerful by the use of schemes to cluster haplotypes into related groups based on parsimony, for example. Templeton and coworkers have demonstrated the power of cladograms for analysis of haplotype data. (Templeton et al. A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping. I. Basic Theory and an Analysis of Alcohol Dehydrogenase Activity in Drosophila Genetics 117: 343-351, 1987. Templeton et al. A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping and DNA Sequence Data. III. Cladogram Estimation Genetics 132: 619-633, 1992. Templeton and Sing. A Cladistic Analysis of Phenotypic Associations With Haplotypes Inferred From Restriction Endonuclease Mapping. IV. Nested Analyses with Cladogram Uncertainty and Recombination. Genetics 134: 659-669, 1993. Templeton et al. Recombinational And Mutational Hotspots Within The Human Lipoprotein Lipase Gene. Am J Hum Genet. 66: 69-83, 2000). These analyses describe a set of rules for clustering haplotypes into hierarchical groups based on their presumed evolutionary relatedness. This phylogenetic trees can be constructed using standard software packages for phylogenetic analysis such as PHYLIP or PAUP (Felsenstein, J. Phylogenies from molecular sequences: inference and reliability. Annu Rev Genet. 22:521-65, 1988; Retief, J. D. Phylogenetic analysis using PHYLIP. Methods Mol. Biol. 132:243-58, 2000), and hierarchical haplotype clustering can be accomplished using the rules described by Templeton and co-workers. The methods described by Templeton and colleagues further provide for a nested analysis of variance between different haplotype groups at each level of clustering. The results of this analysis can lead to identification of polymorphic sites responsible for phenotypic variation, or at a minimum narrow the possible phenotypically important sites. Thus, methods for determination of haplotypes have great utility in studies designed to test association between genetic variation and variation in phenotypes of medical interest, such as disease risk and prognosis and response to therapy.
Currently available methods for the experimental determination of haplotypes, particularly methods for the determination of haplotypes over long distances (e.g., more than 5 kb), are based primarily on PCR amplification techniques. One haplotyping method currently in use is based on allele specific amplification using oligonucleotide primers that terminate at polymorphic sites (Newton et al. Amplification Refractory Mutation System For Prenatal Diagnosis And Carrier Assessment In Cystic Fibrosis. Lancet. December 23-30; 2 (8678-8679):1481-3, 1989; Newton et al., Analysis Of Any Point Mutation In DNA. The Amplification Refractory Mutation System (ARMS). Nucleic Acids Res. Vol. 17, 2503-2516, 1989). The ARMS system was subsequently further developed (Lo, Y. M. et al., Direct haplotype determination by double ARMS: specificity, sensitivity and genetic applications. Nucleic Acids Research July 11:19 (13):3561-7, 1991) and has since been used in a number of other studies. ARMS is the subject of U.S. Pat. Nos. 5,595,890 and 5,853,989. This method requires the amplification of long DNA segments. In addition, different primers and assay conditions for allele specific amplification must be established for each polymorphic site that is to be haplotyped. For example, consider a locus with five polymorphic sites. Subject A is heterozygous at sites 1, 2 and 4; subject B at sites 2 and 3, and subject C at sites 3 and 5. To haplotype A requires allele specific amplification conditions from sites 1 or 4; to haplotype B requires allele specific amplification conditions from sites 2 or 3, and to haplotype C requires allele specific amplification conditions from sites 3 or 5 (with the allele specific primer from site 3 on the opposite strand from that used to haplotype B).
A similar method for achieving allele specific amplification takes advantage of some thermostable polymerases' ability to proofread and remove a mismatch at the 3′ end of a primer. Primers are designed with the 3′ terminal base positioned opposite to the variant base in the template. In this case the 3′ base of the primer is modified in a way that prevents it from being extended by the 5′-3′ polymerase activity of a DNA polymerase. Upon hybridization of the end-blocked primer to the complementary template sequence, the 3′ base is either matched or mismatched, depending on which alleles are present in the sample. If the 3′ base of the primer is properly base paired the polymerase does not remove it from the primer and thus the blocked 3′ end remains intact and the primer can not be extended. However, if there is a mismatch between the 3′ end of the primer and the template, then the 3′-5′ proofreading activity of the polymerase removes the blocked base and then the primer can be extended and amplification occurs.
Other allele specific PCR amplification methods include further methods in which the 3′ terminal primer forms a match with one allele and a mismatch with the other allele (U.S. Pat. No. 5,639,611), PCR amplification and analysis of intron sequences (U.S. Pat. No. 5,612,179 and U.S. 5,789,568), or amplification and identification of polymorphic markers in a chromosomal region of DNA (U.S. Pat. No. 5,851,762). Further, methods for allele-specific reverse transcription and PCR amplification to detect mutations (U.S. Pat. No. 5,804,383), and a primer-specific and mispair extension assay to detect mutations or polymorphisms (PCT/CA99/00733) have been described. Several of these methods are directed to genotyping, not to haplotyping.
Other haplotyping methods that have been described are based on analysis of single sperm cells (Hubert et al. Sperm Typing Allows Accurate Measurement Of The Recombination Fraction Between D3S2 And D3S3 On The Short Arm Of Human Chromosome 3. Genomics. 1992 April;12(4):683-687); on limiting dilution of a DNA sample until only one template molecule is present in each test tube, on average (Ruano et al. Haplotype Of Multiple Polymorphisms Resolved By Enzymatic Amplification Of Single DNA Molecules. Proc Natl Acad Sci USA 1990 87(16):6296-6300); or on cloning DNA into various vectors and host microorganisms (U.S. Pat. No. 5,972,614).
The pattern of genetic variation in most species, including humans, is not random; as a result of human evolutionary history some sets of polymorphisms occur together on chromosomes, so that knowing the sequence of one polymorphic site may allow one to predict with some probability the sequence of certain other sites on the same chromosome. Once the relationships between a set of polymorphic sites have been worked out, a subset of all the polymorphic sites may be used in the development of a haplotyping test. The polymorphisms that comprise a haplotype may be of any type. Most polymorphisms (about 90% of all DNA polymorphisms) involve the substitution of one nucleotide for another, and are referred to as single nucleotide polymorphisms (SNPs). Another type of polymorphism involves a change in the length of a DNA segment as a result of an insertion or deletion of anywhere from one nucleotide to thousands of nucleotides. Insertion/deletion polymorphisms (also referred to as indels) account for most non-SNP polymorphisms. Common kinds of indels include variation in the length of homopolymeric sequences (e.g., AAAAAA vs. AAAAA), variation in the number of short tandem repeat sequences such as CA (e.g., 13 repeats of CA vs. 15 repeats), and variation in the number of more complex repeated sequences (sometimes referred to as VNTR polymorphisms, for variable number of tandem repeats), as well as any other type of inter-individual variation in the length of a given DNA segment. The repeat units may also vary in sequence.
ApoE
Apolipoproteins are found on the surface of various classes of lipoproteins—membrane bound particles which transport lipids (mainly cholesterol and triglycerides) throughout the body, including the brain. The function of apolipoproteins is to direct lipoproteins to specific cells that require lipids, for example cells that store fat. The apolipoproteins bind to specific receptors on the surface of lipid requiring cells, thereby directing the transport of lipids to the target cell. Apolipoprotein E (ApoE) is one of about a dozen apolipoproteins on blood lipoproteins, but it is the major apolipoprotein in the brain. One important function of ApoE in the brain is to transport lipids to cells that are performing membrane synthesis, which often occurs as a response to acute or chronic brain injury. After injury there is usually extensive synaptic remodeling as the surviving neurons receive new inputs from cells that were formerly wired to injured cells. This neuronal remodeling, or plasticity, is an important part of the physiologic response to the disease process and modulates the course of disease. Patients with low ApoE levels or impaired ApoE function have impaired neuronal plasticity.
Variation at the ApoE gene has been associated with risk of Alzheimer's disease (AD) and other neurodegenerative diseases, recovery or protection from organic or traumatic brain injury, and response to pharmacotherapy of AD. In Alzheimer's disease one injured brain region is the cholinergic pathways of the basal forebrain and elsewhere. The degree of neuronal remodeling in such areas may affect the response to cholinomimetic therapy. Thus impaired brain lipid transport alters patterns of neuronal remodeling in cholinergic (and other) pathways and thereby potentially affects response to acetylcholinesterase inhibitors and possibly other cholinergic agonists.
Variation at the ApoE gene has also been associated with coronary heart disease, dyslipidemia, and immunomodulatory functions. Specific apolipoprotein E genotypes have been associated with high cholesterol and LDL-cholesterol levels, and may serve as an independent predictors of coronary events. ApoE genotypes and haplotypes may identify individuals that are at risk of developing coronary artery disease (CAD) at an earlier age of onset, are more susceptible to developing lipidemia following environmental exposure (to infection, drug treatment or diet), of developing lesions at an accelerated rate, or of developing more severe signs of disease pathology or symptoms. In clinical studies in the cardiovascular area, apoE haplotyping may be used to identify patients at risk for CAD and thus differentiate candidates for dietary, pharmacologic or surgical intervention. ApoE haplotyping may identify individuals at risk for earlier coronary artery bypass graft (CABG) intervention. ApoE may interact synergistically with additional genes that contribute significantly to developing pathology in CAD, including other lipoproteins containing apoB, apoC, apoj, and other genes involved in lipid metabolism, such as OATP2, CETP, LPL, FABP2, ABC1, CYP7 and PON. Since CAD can develop from underlying and chronic conditions such as hypertension, apoE may serve as a gene that contributes to diagnosis or treatment guidelines along in combination with other genetic markers, for example, apoE and PAI-1, AGT and AT1-receptor.
ApoE also modulates the accumulation of cholesterol in macrophages and their transition to foamy cells as well as formation of the fatty streak pathology of atherosclerosis. The role of apoE in modulating the immune response and inflammatory cytokine network may be a therapeutic strategy to slow progression or reverse pathological lesions caused by foamy cell activation. ApoE genotypes may differentiate interactions on specific cells, for example, endothelial cell or glial cell subtypes. The overlapping role of apoE in macrophage biology and nerve repair suggests that apoE may be a marker for increased risk of developing peripheral neuropathies, such as diabetic peripheral neuropathy or retinopathy. Furthermore, apoE may be an independent risk factor for CAD, independent of cholesterol levels. Apo E genotype may also be associated with peripheral arterial disease (PAD). This association may be expanded by the presence of co-morbid conditions, for example diabetes, which is also associated with dyslipidemia and a predisposition to macrovascular disease. In addition, apoE genotypes may further refine diagnosis of cerebral pathology and cerebrovascular lesions in cerebral amyloid angiopathy, neurodegenerative diseases such as multiple sclerosis, and epilepsy and reparative potential following brain injury in trauma or ischemic stroke events.
The existence of three major variant forms of ApoE (referred to as ε2, ε3 and ε4) has been known for over two decades. The well established three variant classification of ApoE is based on two polymorphisms in the coding sequence of the ApoE gene, both of which result in cysteine vs. arginine amino acid polymorphisms in APOE protein at positions 112 and 158 of the mature protein. DNA based diagnostic tests for ApoE have been available since the 1980s.
The ApoE ε4 allele has been consistently correlated with elevated total cholesterol, elevated LDL cholesterol, low levels of ApoE protein and increased risk of coronary heart disease (CHD). The CHD risk attributable to ε4 is apparent even after correcting for cholesterol levels and other CHD risk factors (smoking, age, obesity, diabetes, blood pressure). Thus, consideration of a subject's ApoE genotype is reasonable for any disease category in which there is hyperlipidemia, hypercholesterolemia, hypertriglyceridemia or any disorder leading to inordinate lipid metabolism. Furthermore, studies in normolipidemic populations have shown an association with apoE variants and increased risk for coronary artery disease. The ε4 allele is also a risk factor for late onset Alzheimer's disease and Multiple Sclerosis (MS), apparently due to effects on the rate of disease progression. Presence of the ApoE ε4 allele also portends a poor prognosis for patients with a variety of other neurological diseases (stroke, brain trauma, amyotrophic lateral sclerosis and other diseases) and psychiatric diseases (e.g., schizophrenia), compared to patients without an ε4 allele.
In addition to effects on disease risk and disease prognosis there are reports that ApoE genotype predicts response of AD patients to medications. In particular, the response of AD patients to acetylcholinesterase inhibitors has been studied by several groups. ApoE genotype may also be useful for predicting patient response to other medical treatments, particularly treatments for neurological and cardiovascular diseases. The ApoE ε4 variant is a major risk for Alzheimer's disease, perhaps because it is expressed in brain at lower levels than the ε2 or ε3 variants, and thus impairs neuronal remodeling. The ε2 allele is mildly protective for AD. Several clinical trials for Alzheimer's disease drugs, including both acetylcholinesterase inhibitors and vasopressinergic agonists, have shown significant interactions with ApoE genotype and sex. The ε4 allele has been associated with lack of response to acetylcholinesterases.
The relative risk of AD conferred by the ε4 allele varies almost ten fold between different populations. The highest relative risk has consistently been reported in the Japanese, who have a 30-fold relative risk in ε4/ε4 homozygotes relative to ε3/ε3 homozygotes. African and Hispanic ε4/ε4 homozygotes have relative risks of only ˜3-4 fold. On the other hand, in the presence of an ε4 allele the cumulative risk of AD to age 90 is similar in all three groups (Japanese, Hispanics and Africans). This suggests that other factors contribute significantly to the causation of AD in the non-Japanese populations. It may be that these non-ε4 AD patients are the best responders to acetylcholinesterases. If true, this may account for a lack of response in Japanese, where the fraction of patients with ApoE ε4 mediated AD appears to be the highest in the world.
It is well established that the three common variants at the ApoE locus are correlated with risk of AD in various populations. Recent studies have also shown that ApoE genotype correlates with response of AD patients to two classes of drugs. Specifically, Poirier et al. demonstrated an interaction of apoE genotype, sex and response of AD patients to the cholinomimetic drug tacrine, while Richard et al. showed an interaction between apoE genotype and response to an investigational noradrenergic/vasopressinergic agent, S 12024. In both studies the analysis was restricted to analysis of the two amino acid variances that determine the three common ApoE variants. Other variances have been described at the ApoE locus, including promoter variances, that may plausibly affect ApoE function. Also, studies have been published (but often not confirmed) associating polymorphisms in other genes with risk of late onset AD; there have been no investigations of the effect of variation at these loci on response to cholinomimetic drugs.
There are two FDA approved drugs for therapy of Alzheimer's Disease (tacrine, donezepil), and at least a dozen additional agents in late stage clinical trials or under FDA review. The FDA approved drugs work by inhibiting acetylcholinesterase, thereby boosting brain acetylcholine levels. This symptomatic therapy provides modest benefit to less than half of treated patients but does not affect disease progression. Available evidence suggests the products in the pipeline, which likewise partially reverse symptoms without affecting the underlying disease process, will also be of modest benefit to some patients. Despite their limited efficacy, these drugs will likely be expensive. They may also be associated with serious adverse effects in some patients. As a result, the cost of providing a modest benefit to a limited number of AD patients will be high.
As more AD therapeutics becomes available, physicians will face the difficult task of differentiating between multiple products. These products may produce similar response rates in a population, however, the crucial decision clinicians face is selecting the appropriate therapeutic for each individual AD patient at the time of diagnosis. This is particularly the case if there are several therapeutic choices, only one of which may be optimal for a particular patient. This selection is critical because failure to provide optimal treatment at the time of diagnosis may result in a diminished level of function during a period when the greatest benefit could be achieved. Inadequate treatment may continue for some time because measures of clinical response in AD are notoriously imprecise; six months or longer may pass before it is clear whether a drug is working to a significant degree. During this time, the disease continues to progress which may limit the efficacy of a second drug or therapeutic regimen. A test that could predict likely responders to one or more AD drugs would thus be of great value in optimizing patient care and reducing the cost of ineffective treatment.
Data has been published suggesting that ApoE genotype may be such a test. Specifically, Farlow, Poirier and colleagues have shown that female patients with the ApoE ε4 allele do not respond to tacrine, while female patients with the ε2 and ε3 alleles have significant response; males do not respond significantly regardless of genotype. Conversely, Richard et al. have demonstrated that patients with the ε4 allele, but not the ε2 and ε3 alleles, have a statistically significant response to S12024, an enhancer of vasopressinergic/noradrenergic signaling. Thus the two drugs—one an acetylcholinesterase inhibitor and the other a vasopressinergic/noradrenergic agonist—are useful in different groups of patients, delimited by ApoE genotype.
ApoE gene activity or allele variants are known to alter the course of several other neurological diseases. In multiple sclerosis, the relative concentration of ApoE is reduced in cerebrospinal fluid as well as intrathecal synthesis. Other neurological disorders such as temporal lobe epilepsy and cerebral trauma, the presence of the ApoE ε4 variant is associated with increased vulnerability to disease progression, whereas presence of ApoE ε3 appears to provide moderate neuroprotection. Wilson's disease, a disorder of the biliary copper excretion that may result in severe neurological symptoms and advanced liver, was the subject of a study that examined the ApoE genotype as well as the H1069Q mutation (the most common mutation identified in Wilson's disease). The presence of ApoE ε3/ε3 attenuates the clinical manifestations in Wilson's disease by a proposed mechanism of antioxidant and membrane stabilizing properties of ApoE ε3 protein.
In patients undergoing routine ambulatory peritoneal dialysis (CAPD), it has been shown that these patients develop various abnormalities of lipid metabolism and are prone to develop accelerated atherosclerosis. It has been shown that the ApoE ε3/ε3 genotype appears to the most common genotype in CAPD and that the ApoE ε2/ε3 genotype appears to be associated with high cholesterol and triglyceride levels.
Recent data has suggested that there is an association between the ApoE epsilon variant and reduced risk of age related macular degeneration.
Glycogen storage disease type Ia patients have elevated serum triglyceride concentrations and VLDL as well as LDL fractions but only moderately elevated phospholipid and cholesterol levels. In a recent study, the ε3 and ε4 variants were predominant in patients with glycogen storage disease type Ia and had a high triglyceride binding capacity and thus are thought to increase the triglyceride clearance.
Further, there has been an association of ApoE ε4/ε3 phenotype in persons with non-insulin dependent diabetes mellitus and associated metabolic syndrome X.
However, despite the many genetic associations described above, diagnostic tests for determining ApoE genotype are not widely used, nor is ApoE genotyping widely used for prognostic or pharmacogenetic testing. To the contrary, a large number of studies address the limitations of ApoE as a diagnostic marker, particularly in the setting of AD diagnosis. The conclusion of most of these studies is that testing for the ε2, ε3 and ε4 variants does not provide a sufficiently sensitive or selective test to justify use outside of clinical research. Concern has also been expressed that, because in many settings ApoE testing results do not affect medical decision making, there is little reason to obtain information on ApoE genotype.
Recent studies of the ApoE gene in a number of laboratories have led to identification of several new DNA polymorphisms. The biological effects and medical import of these new polymorphisms has not been established, although some studies suggest that polymorphisms in the promoter affect ApoE transcription rates. Most published work has been limited to the analysis of individual polymorphisms or sets of only a few polymorphisms and their effect on one or two biological or clinical endpoints.
The ability to predict response to therapy for progressive debilitating diseases like AD and others discussed above would be of enormous clinical importance as there is generally only one opportunity to treat patients with these diseases at their maximal level of functioning; any delay in selecting optimal therapy represents a lost opportunity to preserve the maximal possible level of function. With multiple drugs in development for AD as well the other disease indications, it will become increasingly important to predict the best drug for each patient.